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Smart learning ecosystems leverage on state-of-theart tools and technologies to help students learn better with Information Communication Technologies (ICT). The ubiquity, innovations and advancements of ICT have transformed pedagogies and approaches to content facilitation and delivery in higher education worldwide, the Pacific region being no exception. The paper essays a number of learning and support tools designed in-house or adopted (or outsourced) recently by a higher education institution in the Pacific contributing to the smart learning ecosystem. The institution has integrated these ICT driven tools to its academic and support programmes, and more recently the in-country science programmes introduced in its member countries. The strengths and challenges from the implementation of these new adaptive tools are highlighted with recommendations to the wider academic populace.

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Intelligent optimization algorithms based on swarm principles have been widely researched in recent times. The Firefly Algorithm (FA) is an intelligent swarm algorithm for global optimization problems. In literature, FA has been seen as one of the efficient and robust optimization algorithm. However, the solution search space used in FA is insufficient, and the strategy for generating candidate solutions results in good exploration ability but poor exploitation performance. Although, there are a lot of modifications and hybridizations of FA with other optimizing algorithms, there is still a room for improvement. Therefore, in this paper, we first propose modification of FA by introducing a stepping ahead parameter. Second, we design a hybrid of modified FA with Covariance Matrix Adaptation Evolution Strategy (CMAES) to improve the exploitation while containing good exploration. Traditionally, hybridization meant to combine two algorithms together in terms of structure only, and preference was not taken into account. To solve this issue, preference in terms of user and problem (time complexity) is taken where CMAES is used within FA's loop to avoid extra computation time. This way, the structure of algorithm together with the strength of the individual solution are used. In this paper, FA is modified first and later combined with CMAES to solve selected global optimization benchmark problems. The effectiveness of the new hybridization is shown with the performance analysis.

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